In this session, we’ll explore how developing and deploying AI-driven agents demands a fundamentally new testing paradigm—and how scalable simulations deliver the reliability, safety and human-feel that production-grade agents require. You’ll learn how simulations allow you to: - Mirror messy real-world user behavior (multiple languages, emotional states, background noise) rather than scripting narrow “happy-path” dialogues. - Model full conversation stacks including voice: turn-taking, background noise, accents, and latency – not just text messages. - Embed automated simulation suites into your CI/CD pipeline so that every change to your agent is validated before going live. - Assess multiple dimensions of agent performance—goal completion, brand-compliance, empathy, edge-case handling—and continuously guard against regressions. - Scale from “works in demo” to “works for every customer scenario” and maintain quality as your agent grows in tasks, languages or domains. Whether you’re building chat, voice, or multi-modal agents, you’ll walk away with actionable strategies for incorporating simulations into your workflow—improving reliability, reducing surprises in production, and enabling your agent to behave as thoughtfully and consistently as a human teammate.

Personalization at scale needs deep understanding of each customer. You must collect data from many sources, read it, reason and infer, plan, decide, act, and write to each person. One agent doing everything gave us poor and inconsistent quality. Multi-agent systems changed that. They deliver mass personalization. They also break in edge cases, contradict each other, and are hard to debug. I will share how we addressed this with Cortex UCM, a unified customer memory, and Generative Tables. We map noisy data into a clean, structured layer that agents read and write. We began with email for both outbound and inbound communication. Then we personalized websites and product pages for e-commerce at scale. I share customer stories. For example, one customer had over 60,000 product pages that required customization for thousands of communities and product offerings. I will present our decentralized shared-memory orchestration briefly and how it stays transparent and debuggable. It opens safe paths for external agents. What failed. What worked. What we are building next.